Systems and methods for estimating an image marking process using event mapping of scanned image attributes

ABSTRACT

Methods and systems used to automatically identify the marking process used for an image on a substrate based on spatial characteristics and/or color of the image. Image types which are classified and identified include continuous tone images and halftone images. Among halftone images separately identified are inkjet images, xerographic images and lithographic images. Locally adaptive image threshold techniques may be used to determine the spatial characteristics of the image.

BACKGROUND OF THE INVENTION

1. Field of Invention

This invention relates to automatically identifying a marking processused to form an image on a substrate.

2. Description of Related Art

In order to accurately calibrate a scanner, such as, for example, acolor scanner, that scans an image carried on a substrate, differentcalibration transformations are required depending on the markingprocess, such as, for example, photography, inkjet printing, xerography,lithography and the like, and materials, such as, for example, toner,pigment, ink, etc., that are used to form the image on the substrate.For example, a calibration transformation that is used to calibrate thescanner for a photographic image is different from a calibrationtransformation that is used to calibrate the scanner for an inkjet-printed image, which is in turn different from a calibrationtransformation that is used to calibrate the scanner for axerographically-formed image or for a lithographically-formed image.

Typically, a user wishing to scan an image determines the markingprocess used to form the image from prior knowledge of the markingprocess, manually identifies the marking process such as, for example,photographic, ink jet, xerographic or lithographic, and uses the markingprocess information to set the scanner so that an appropriatecalibration can be used. The manual identification is commonly doneusing different descriptions, such as Halftone vs. Photo vs. XerographicCopy on the user interface from which different machine settings areinferred.

Approaches to automatically identifying the marking process aredisclosed in U.S. Pat. Nos. 6,353,675 and 6,031,618, each of which isincorporated herein by reference in its entirety. The approach toautomatically identifying the marking process disclosed in the 618patent uses additional spectral information from the scanned materialobtained through additional spectral channels. The approach used toautomatically identify the marking process disclosed in the 675 patentinvolves an image spatial analyzer that analyzes image datacorresponding to the image to determine at least one spatialcharacteristic based on a power spectrum of the image data and a markingprocess detection system that detects the marking process based on theat least one spatial characteristic.

SUMMARY OF THE INVENTION

It would be desirable to perform analyses of the scanned image datadirectly from the scanned data, that is, without using any additionalresources, to identify the marking process used to form that image. Theinventors have determined that images carried on substrates exhibitunique spatial characteristics that depend upon the type of markingprocess used to form those images.

This invention provides methods and systems that automatically identifya marking process based on spatial characteristics of the marked image.

This invention separately provides systems and methods thatautomatically identify a marking process without the need to add one ormore additional sensors.

This invention separately provides systems and methods thatautomatically identify a marking process without the need to use anyadditional data beyond that obtainable from the marked image using thestandard scanner sensors.

This invention separately provides methods and systems thatautomatically differentiate between continuous tone and binary markingprocesses. Here, it is understood that binary marking processes can beobviously extended to marking processes locally using a small number oflevels as it is done for example in some 7 or 8 head inkjet printingdevices. The terms binary and halftone are used throughout thisapplication to include those systems.

This invention separately provides methods and systems thatautomatically differentiate between different types of binary imagemarking processes, including, for example, inkjet marking processes,xerographic marking processes, and lithographic marking processes.

In various exemplary embodiments of the systems and methods according tothis invention, continuous tone and halftone process images aredifferentiated by examining local variations of the input data,including using local variants as an estimator for local variations ofthe input data. In various other exemplary embodiments of the systemsand methods according to this invention, a point pattern or event map isgenerated for each input data block based on a color level setprocessing technique. In various other exemplary embodiments of thesystems and methods according to this invention, image spatialcharacteristics are identified by checking for halftone dot periodicityin the image. In various other exemplary embodiments of the systems andmethods according to this invention, frequency, frequency relationships,and/or noise characteristics of scanned image data are employed toidentify the image marking process. In various other exemplaryembodiments of the systems and methods according to this invention, adetermination whether or not the image has an underlying halftonerendition with a clustered or dispersed character may be performed.

These and other features and advantages of this invention are describedin, or are apparent from, the following detailed description of variousexemplary embodiments of the systems and methods according to thisinvention.

BRIEF DESCRIPTION OF THE DRAWINGS

Various exemplary embodiments of the systems and methods of thisinvention will be described in detail, with reference to the followingfigures, wherein:

FIG. 1 shows one exemplary embodiment of a decision tree for a mediaidentification process according to the invention;

FIG. 2 shows enlarged views of scanned regions of an image formed usingdifferent image formation processes;

FIG. 3 shows one exemplary embodiment of a decision tree for a mediaidentification process illustrating a correlation between input mediatype and measurable spatial image attributes using statisticaldifferentiators;

FIG. 4 is a flowchart outlining one exemplary embodiment of a method fordetermining the image marking process used to produce an image accordingto this invention;

FIG. 5 is a flowchart outlining in greater detail one exemplaryembodiment of the method for generating data statistics of FIG. 4;

FIGS. 6 and 7 is a flowchart outlining in greater detail one exemplaryembodiment of the method for determining the process used to produce agiven data block of FIG. 4;

FIG. 8 illustrates one exemplary embodiment of a histogram ofinter-minority distance;

FIG. 9 is a functional block diagram of one exemplary embodiment of asystem used to identify media/image marking process according to thisinvention; and

FIG. 10 is a flowchart outlining in greater detail one exemplaryembodiment of the method for generating an event map of FIG. 4.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

The inventors have determined that there is a strong correlation betweenthe input media type and a number of measurable spatial image attributesobtainable directly from the scanned image data itself. Because there isa strong correlation between the input media type and these measurablespatial image attributes, the marking process used to form the scannedoriginal can be ascertained, with a relatively high degree ofconfidence, from the statistical spatial properties of the scanned imagedata.

Typically, photographic printing, as well as any other analog imageprinting process, is a continuous tone, or “contone”, marking process.Binary printing, however, typically involves a halftone process. Inkjetprinting, for example, primarily or typically uses errordiffusion/stochastic screens, while xerography, including colorxerography, primarily or typically uses line-screens and/or clustereddot screens, and lithography primarily or typically uses clustered-dotrotated halftone screens. It should be appreciated that any of thesebinary marking techniques could have one of these halftone processes.However, the choices outlined above are predominant in typical usage,because of image quality and stability considerations.

Black and white images have variations in lightness and darkness. Colorimages have variations in color. Whereas variations in continuous toneimages arise from variations in image data, halftone images havevariations both from the image data and from the halftone reproductionprocess itself. Variations arising from the image data typically occurover much larger scales than the variations occur in halftone processes.Therefore, over a small scale, continuous tone images, such asphotographic images, typically have a much smaller variation than dohalftone images. Based on this, various exemplary embodiments of thesystems and methods according to this invention look at local variationswithin the scanned image data to identify which marking process was usedto render the image. That is, various exemplary embodiments of thesystems and methods according to this invention look at local variationswithin the scanned image data to determine whether a continuous tone orphotographic image marking process was used, or whether a halftonemarking process was used. That is, in various exemplary embodiments ofthe systems and methods according to this invention, continuous toneimage marking processes are differentiated from halftone image markingprocesses by examining local variations of the marked image input data.

FIG. 1 illustrates one exemplary embodiment of a decision tree 100usable to perform image marking process/media identification accordingto the invention. In the decision tree 100 shown in FIG. 1, all imagedata 105 is evaluated. The first decision point 110 differentiatesbetween a continuous tone image marking process 120 and a halftone imagemarking process 125 in a scanned image by examining local variations ofthe scanned image input data to determine whether there is lowlocal/spatial variation 115 in the scanned image data or highlocal/spatial variation 116 in the scanned image data.

This distinction coincides with the distinction between a photograph orother analog image marking process and a binary image marking process.That is, determining continuous tone image data would imply that theimage marking process for the scanned image data is a photo process,i.e., that the image is a photo 121.

Detecting a halftone marking process 125 would imply that the imagemarking process for the scanned image data is an ink-jet marking process140, a xerographic marking process 145, an offset marking process 146,or the like.

In the exemplary embodiment of the decision tree 100 shown in FIG. 1,the next decision point 130 differentiates between the various halftoneimage marking processes 140, 145 and 146 by examining the spatialcharacteristics of the scanned image data to determine whether the datahas a dispersed/aperiodic character 135 or a clustered/periodiccharacter 136.

Detecting data having a dispersed/aperiodic character would imply thatthe image marking process for the scanned image data is an ink-jetmarking process 140, i.e., that the image is an ink-jet image 141. Onthe other hand, detecting data having a clustered/periodic characterwould imply that the image marking process for the scanned image data isa xerographic marking process 145, an offset marking process 146, or thelike.

In the exemplary embodiment of the decision tree 100 shown in FIG. 1,the next decision point 150 differentiates between a xerographic markingprocess 160 and an offset marking process 165 by examining the datafrequency distribution or internal structure of the scanned image data.Image data internal structure examples that may be considered includedetermining whether the image data has a line structure as contrastedwith a rotated structure, whether the halftone dots have a highfrequency structure versus a low frequency structure, and whether thehalftone screen noise is high or low.

Detecting image data having a low frequency/high noise character 155would imply that the image marking process for the scanned image data isa xerographic marking process 160 that was used to create a xerographicimage 161. On the other hand, detecting image data having a highfrequency/low noise character 156 would imply that the image markingprocess for the scanned image data is an offset, or lithographic,marking process 165 that was used to generate an offsetprinted/lithographic image 166.

The decision tree of FIG. 1 is not intended to imply that data can notbe reevaluated. In some cases, for example, data identified as ink-jet141 might still be evaluated with respect to the data frequencydistribution 150 and the result of this being used to verify, harden orreexamine the identification of the marking process of the image as anink-jet marking process 140. The additional burden with respect tospeed, processing time, etc. for verification is system dependent andmight be negligible, in which case reexamination is advantageous. Inother cases, a strict structure like the one shown in FIG. 1 isadvisable. In addition, as will be appreciated by those skilled in theart, the decision process can be applied to the entire image as a singledetermination or can be applied individually or to parts of the image.These independent image portions may be determined by segmenting theimage through an independent process. Furthermore, the decision processmay be independently applied to small regions of the image and theresults from these regions may then be pooled or combined to determinean image marking process. This pooling or combination can further use ameasure of confidence for each region when determining the overallmarking process.

FIG. 2 shows in detail a number of scanned regions of a photograph 210,an inkjet marked image region 220, a lithographically-formed imageregion 230 and a xerographically-formed image region 240, scanned, forexample, at 600 dots per inch (dpi). As shown in FIG. 2, the continuousor photographic image region 210 has a much smaller variation in thenumber of adjacent light and dark areas throughout the scanned regionthan do the halftone-type image regions 220-240. Additionally, as shownin FIG. 2, the halftone dots of the inkjet image region 220 have anaperiodic dispersed nature, while the halftone dots of thelithographically-formed image region 230 and the xerographically-formedimage region 240 have strong periodic structures. Finally, as shown inFIG. 2, the lithographically-formed image region 230 has a higherspatial frequency of halftone dots and lower noise than does thexerographically-formed image region 240.

FIG. 3 is a decision tree illustrating the correlation of the scannedimage data with the input media determination process of FIG. 1 usingstatistical differentiators at each decision point 310, 320 and 330. Inthe exemplary embodiment shown in FIG. 3, just as in the exemplaryembodiment shown in FIG. 1, the first decision block 310 differentiatesbetween analog tone and binary image marking processes. As shown in FIG.3, this is achieved by examining the local variations of the input data.An image formed by a binary image marking process typically shows arelatively high level of local variation compared to an image formedusing an analog image marking process, such as a continuous tone imagemarking process, such as, for example, a photographic, image markingprocess. Accordingly, local feature variants may be used as an estimatorfor the local variation at this stage. As a result of the analysis inthe first decision block 310, images created using an analog orcontinuous tone image marking process 315, such as, for example a photoimage marking process 315, are separated from images created using otherimage marking processes.

The second decision block 320 of FIG. 3 differentiates between an inkjetimage forming process 325 and other halftone image marking processes,such as, for example, a xerographic image marking process 335, an offsetor lithographic image marking process 345, or the like. This isaccomplished by examining various spatial characteristics of the scannedimage data to determine whether the data has a dispersed/aperiodiccharacter or a clustered/periodic character. In various exemplaryembodiments, the second decision block 320 differentiates between aninkjet image marking process 325, and a xerographic image markingprocess 335 or an offset image marking process 345 by evaluating therendering uniformity and periodicity of the observed spatial variationof the halftone dots to discriminate between clustered and dispersed dotrendering methods.

For example, inkjet-formed marking processes 325 use mainly distributeddot techniques, such as, for example, error diffusion, stochasticscreening and/or blue noise screening. These processes commonly do nothave a single fundamental periodicity across all gray levels. However,distributed dot techniques are extremely uncommon for xerographic imagemarking processes 335 or for lithographic or offset image markingprocesses 345. Xerographic image marking processes 335 and lithographicor offset image marking processes 345 typically use clustered halftonedot techniques that have a dot periodicity that is not a function of theinput level. At the same time, distributed dot techniques have a higheruniformity than do clustered dot techniques.

The third decision block 330 of FIG. 3 differentiates betweenxerographic image marking processes 335 and offset or lithographic imagemarking processes 345 by analyzing frequency and noise characteristicsof the scanned data. In one exemplary embodiment, the third decisionblock 330 differentiates between xerographic image marking processes 335and offset or lithographic image marking processes 345 by evaluating thesymmetry and frequency of the halftone dots. In general, line screensare common in xerographic image marking processes 335, but are uncommonin offset or lithographic image marking processes 345. Rotated dotschemes are also common in xerographic image marking processes. Based onthese tendencies, the absolute frequency of the input screen, and itsnoise characteristics can be analyzed as part of the third decisionblock 330. In particular, high frequency, low noise screens may beassociated with offset or lithographic image marking processes 345,while low frequency, high noise screens may be associated withxerographic image marking processes 335.

As noted above, in various exemplary embodiments of the systems andmethods according to this invention, a group of pixels from a fairlysmall block or sub-region that may be considered to be roughlyhomogenous in terms of color or gray value can be examined. Since theimage has no spatial variation over a homogeneous region, the spatialstructure in the halftoned version of the image is entirely due to thehalftoning technique. Such regions are therefore useful for analyzingthe underlying halftone technique without interference from the imagecontent. Often binarizing a related group of pixels in the block willreveal the spatial arrangements that take place in the image markingprocess, that is, halftone marking process or continuous tone markingprocess. Accordingly, in various exemplary embodiments of the systemsand methods according to the invention, a block of a related group ofimage pixels is binarized to create a map that is indicative of imagemarking processes.

FIG. 4 is a flowchart outlining one exemplary embodiment of a method fordetermining from scan image data of an image, the image marking processused to create an image according to this invention. As shown in FIG. 4,the method begins in step S1000, and continues to step S1100, where thescanned image data is divided into one or more data blocks, each havinga determined number of pixels. In various exemplary embodiments of themethods and systems according to this invention, the scanned image datamay be divided into data blocks or areas having any desired number ofpixels. In one exemplary embodiment, the scanned image data may bedivided into data blocks having 60×60 pixels for scanned images at 600dpi. This division into blocks could be based on pure spatialconsiderations, e.g. location, but might also be influenced byadditional information such as given by image segmenters and the like.

Then, in step S1200, the one or more image data blocks are selected tobe analyzed or processed. In various exemplary embodiments of themethods and systems according to this invention, to obtain low-noisedata, data blocks or areas that represent constant or near constantimage data are preferably selected in step S1200. This tends to excludeimage edges, paper background, and the like.

Next, in step S1250, the selected one or more image data blocks areprocessed to create or generate an event map or a point pattern. Theevent maps or point patterns display the regular and periodic structureof spatial dot arrangements resulting from xerographic and lithographicmarking processes, and the dispersed and aperiodic structure of dotsfrom inkjet marking process.

Then, in step S1300, each of the selected one or more image data blocksin the event maps/point patterns generated is processed to generate oneor more data statistics for that image data block. In various exemplaryembodiments of the methods and systems according to this invention, theone or more data statistics generated for the one or more image datablocks may include determining an average or mean value of the pixelsfor the image data block being processed, determining a variance valueof the pixels for the image data block, determining the extremes, suchas, for example, the minimum value, min_(a), and maximum value, max_(a),of the pixels for the image data block, generating histograms of thedata being processed, and performing various data evaluations using thedetermined statistical values and histograms. To estimate if the inputhas significant and consistent periodicity, it is particularlybeneficial to locate local minima along traversals through the imageblock, determine the distances between successive minima, and determinehistograms of these inter-minima distances. A strong peak in a histogramof inter-minimum distances indicates that a large number of minima areseparated by a constant period, thereby implying periodicity. Localmaxima can similarly be used, and a decision between the use of minimaand maxima may be made based on image level, for instance. Operationthen continues to step S1400.

In step S1400, the one or more data statistics generated for the one ormore image data blocks are compared with image data statistics alreadydetermined and provided in an image data statistics model. Next, in stepS1500, the results of comparing the one or more data statisticsgenerated in step S1300 for the one or more image data blocks are usedto determine the specific image marking process used to format theimage. Operation then continues to step S1600, where operation of themethod stops.

It should be appreciated that, in various exemplary embodiments, stepS1400 can be omitted. In this case, operation of the method wouldproceed directly from step S1300 to step S1500. In general, step S1400can be skipped.

FIG. 10 is a flowchart outlining in greater detail one exemplaryembodiment of the method for generating or creating an event map orpoint patterns of FIG. 4. As shown in FIG. 10, two exemplary embodimentsof the method for creating an event map may be employed.

In one exemplary embodiment, operation the method begins in step S1250and continues to step S1255, where frequency distribution of colors inthe image input data block is determined. Next, the method continues tostep S1265 where a color level set is generated. Then, in step S1299,operation returns to step S1300. This approach is based on the fact thata group of dots with a certain value, when viewed separately from dotswith other colors, can represent the spatial underlying halftone value.This is equivalent to viewing a color level set of the color block, asdescribed in the following paragraph.

If one assumes an image block area under study A⊂R² corresponds tospatial domain of a small block image {right arrow over (I)} defined as:{right arrow over (I)}: A→R³. If color vector in R³ is a color levelset, LS_(c) (x, y), is given by those spatial points {(x, y)εR²:I(x,y)={right arrow over (c)}}. This color level set, produces a point setwhere the event locations, i.e., the dots pixels corresponding to theselected color, are set to 1, whereas the remaining locations are set to0. selection of a representative color value can be carried out byexamining the frequency distribution of colors, that is the colorhistogram H_(c): R³→N. (N: natural numbers), of the block.

The scanned output produced by an input scanner for halftone input (notshown) has three separate channels, of which red channel correspondsapproximately to cyan dots, green channel corresponds to magenta dots,and blue channel corresponds to yellow dots of the input media placed onthe platen. Digital image data takes values not in real numbers but inintegers, from 0 to 255. Each channel's gray values in [0, 255] of thecolor space [0,255]³≡[0,255]×[0,255]×[0,255] is binned into N intervals,then an N³ number of color cubes whose sides are of length Δ=225/N canbe used to find the color histogram H_(c) of the image block. Each color{right arrow over (c)}=(c₁, c₂, c₃)ε[0,255]³ falls into a color cube,and counting the frequency of color cubes used by the block results inthe histogram of colors in the block. If there are n_(c) number of colorcubes {right arrow over (c)}_(i), i=1, . . . , n_(c) for a given block,a color level set corresponding to a specific color cube (e.g., colorwith maximum frequency, or color whose frequency is close to meanfrequency) is given by:

$\begin{matrix}{{{LS}( {x,y} )} = \{ \begin{matrix}{1,\{ {{( {x,y} )\text{:}{\overset{arrow}{I}( {x,y} )}} \in {\overset{arrow}{c}}_{i}} \}} \\{0,\{ {{( {x,y} )\text{:}{\overset{arrow}{I}( {x,y} )}} \notin {\overset{arrow}{c}}_{i}} \}}\end{matrix} } & (1)\end{matrix}$

In an alternative exemplary embodiment, an event map or point sets maybe created or obtained by generating a level set from each image channelseparately. As shown in FIG. 10, operation the method begins in stepS1250 and continues to step S1275, where a grey value histogram of animage channel is generated. Next, the method continues to step S1285where a threshold value for each image channel is determined. Afterdetermining the threshold grey value, the level set for a color levelset is generated in step S1295. Then, in step S1299, operation returnsto step S1300.

In the alternative exemplary embodiment, gray value histogram of achannel is utilized, and a threshold value calculated from the histogramis used to generate a binary set. For a color block, the gray histogramof each image channel can be assumed to be bi-modal except for verylight and very dark intensity blocks. This assumption relies on the factthat halftone dot structure usually manifests itself in terms ofminority gray values on majority background values. Thus, the histogramwill generally be bi-modal and asymmetric.

The threshold level from the normalized histogram H:[0,255}→[0,1] (realinterval) may determined as follows. First, to overcome noise effects, alower gray value g_(low)ε[0,255], and an upper gray valueg_(up)ε[0,255], are determined to compute the gray intensity range ofthe block. For instance, g_(low) and g_(up) correspond to 2 percentileand 98 percentile gray values, i.e., for gε[0,255],

∫₀^(g_(low))H(g) 𝕕g = 0.02, and  ∫₀^(g_(up))H(g) 𝕕g = 0.98.Then gray value that is in the middle range can be found as the averageof these two values:

$g_{mid} = {\frac{g_{low} + g_{up}}{2}.}$Similarly, the 50 percentile gray value is determined by g50:

∫₀^(g 50)H(g) 𝕕g = 0.50.Since a gray histogram is bi-modal without symmetry, it is expected thatg_(mid) and g50 to have an offset value g50−_(mid). Picking(g50+g_(mid))² as a threshold value separates the histogram into twoparts. However, in order to extract spatial pattern of those pixelswhose intensity values fall into minority cluster in the histogram, athreshold value that corresponds to g_(thresh)=2g_(mid)=g50 may be usedas an exemplary embodiment of the system and method of this invention.After finding the threshold gray value, the point set of a single imagechannel, I(x,y), is obtained from the lower (or upper) level set definedas:

$\begin{matrix}{{{LS}( {x,y} )} = \{ \begin{matrix}{1,\{ {{( {x,y} )\text{:}{I( {x,y} )}} < g_{thresh}} \}} \\{0,{\{ {{( {x,y} )\text{:}{I( {x,y} )}} \geq g_{thresh}} \}.}}\end{matrix} } & (2)\end{matrix}$

As discussed above, Applicants have found that the point patternsclearly display the regular and periodic structure of spatial dotarrangements resulting from xerographic and lithographic printingprocess and the dispersed and periodic structure of dots from inkjetprinting process.

FIG. 5 is a flowchart outlining in greater detail one exemplaryembodiment of the method for generating the data statistics of FIG. 4.As shown in FIG. 5, operation of the method begins in step S1300 andcontinues to step S1310, where statistical values or parameters aredetermined over the selected data block or pixel area. In variousexemplary embodiments, any or all of a number of statistical values orparameters may be determined, such as, for example, an area average ormean <A> of the pixels for the image data block, an area variance σ_(a)of the pixels for the image data block, and the extreme minima andmaxima values, min_(a) and max_(a) of the pixels for the image datablock. The determined statistical values or parameters may be determinedusing well known spatial statistics methods or techniques.

Then, in step S1320, various data evaluations are performed using thedetermined statistical values or parameters. In one exemplary embodimentof the methods and systems according to this invention, data evaluationsmay include determining a ratio of the area variance σ_(a) to mean <A>determined for a given block, determining the distribution of the meanvalues <A> for large pixel areas, comparing the determined mean value<A> to the determined min_(a) and/or max_(a) values, determining adistance between local maxima/minima, and the like.

Next, in step S1330, histograms are generated using the results of thedata evaluations performed in step S1320. Then, in step S1340, operationreturns to step S1500.

FIGS. 6 and 7 illustrate a flowchart outlining in greater detail oneexemplary embodiment of determining the image marking process of FIG. 4.As shown in FIGS. 6 and 7, operation of the method begins in step S1500and continues to step S1505, where local variations in image data areevaluated to distinguish between a continuous tone image marking processand other halftone marking processes. In various exemplary embodimentsof the methods and systems according to this invention, in step S1505,area variance is used as an estimator for local variation in the imagedata. In various exemplary embodiments, the area variance to mean ratiois used to evaluate local variations in the image data. The areavariance to mean ratio is directly used to distinguish halftone markingprocesses from a continuous tone marking process or background areas, asdiscussed below.

Then, in step S1510, a determination is made whether the image dataevaluated exhibits high local variation. As discussed above, acontinuous tone image, for example, a scanned photographic image,exhibits a much smaller local variation than halftone images, such as,for example, an inkjet-formed image, a xerographically-formed image or alithographically-formed image. If the image data does not exhibit highlocal variation, it is likely that the image marking process used toform the image is a continuous tone image marking process or the imagedata contains significant background noise. It should be noted that inany image marking process, some local areas might exhibit low variance,for example in image highlight and shadow regions, or in other solidcolor areas. Accordingly, if the image data does not exhibit high localvariation, operation continues to step S1515. If image data exhibitshigh local variation, operation continues to step S1535.

As shown in FIG. 7, in step S1515, a distribution of the mean value overlarge data blocks/areas is determined or analyzed to distinguish betweena continuous tone image marking process and background noise. Next, instep S1520, a determination is made whether the distribution of the meanvalue is characteristic of a continuous tone marking process. If so,operation continues to step S1525. Otherwise, operation jumps to stepS1530. In step S1525, the image marking process is identified as ordetermined to be a photographic image marking process. Operation thenjumps to step S1570. In contrast, in step S1530, the image data isidentified and/or classified as background noise. Operation then alsojumps to step SI570. It should be appreciated, that, if the backgrounddata blocks were not suppressed, their classification as “photo” datablocks could swamp all rendering-derived image signatures.

As shown in FIG. 6, in step S1535, the image data is evaluated for itsperiodicity characteristics. In various exemplary embodiments of themethods and systems according to this invention, in step S1535, the datablock mean value is compared to the determined min_(a) and max_(a)values to distinguish the minority pixels in the distribution. Theminority pixels are generally either light pixels on a dark backgroundor dark pixels on a light background. This distinction is made as noisesuppression, such that only minority pixels are analyzed further becausethe halftone characteristics are better identified by considering thedistribution of the minority pixels.

Next, in step S1540, a determination is made whether the evaluated imagedata has a clustered character with high periodicity. If image data doesnot have high periodicity, operation continues to step S1545. Otherwise,operation jumps to step S1550. In step S1545, the image marking processused to create the scanned image is determined to be an inkjet imagemarking process. As discussed above, inkjet-based marking processes usemainly distributed dot techniques, such as, for example, errordiffusion, stochastic screening, frequency modulation, and/or blue noisescreening, which do not have a single fundamental periodicity across allgray levels. Operation then jumps to step S1570.

In contrast, in step S1550, the frequency and noise characteristics ofthe scanned image data are evaluated to further distinguish between axerographic image marking process and an offset-marking process. Invarious exemplary embodiments of the methods and systems according tothis invention, in step S1550, the absolute frequency of the inputscreen is determined and the noise characteristics of the screen areexamined. In one exemplary embodiment, in step S1550, after the minoritypixels are identified, the distance between maxima/minima correspondingto subsequent minority pixels is determined, excluding a small regionaround the mean to exclude noise.

Next, it step S1555, a determination is made whether the scanned imagedata has a low frequency, high noise character. If so, operationcontinues to step S1560. Otherwise, operation jumps to step S1565. Instep S1560, image marking process is determined to be, and/or isidentified as, a xerographic image marking process. Operation then jumpsto step S1570. In contrast, in step S1565, the image marking process isdetermined to be, and/or is identified as, an offset image markingprocess because high frequency, low noise screens are correlated withoffset input. Operation then continues to step S1570, where theoperation of the method returns to step S1600.

FIG. 8 illustrates one exemplary embodiment of a histogram of theinter-maxima/minima distance between minority pixels for a single imagearea formed using an inkjet image marking process, axerographically-formed image marking process and an offset image markingprocess, based on the results generated in step S1500 of FIG. 4. Asshown in FIG. 8, different media types may be distinguished. Forexample, the inkjet image marking process curve 630 is clearlydistinguishable, having a strongly different frequency characteristicwith no clear periodicity. On the other hand, the offset image markingprocess curve 610 and the xerographically-formed image marking processcurve 620 both show strong periodicity.

Further, as shown in FIG. 8, the offset image marking process curve 610and the xerographic image marking process curve 620 are furtherdistinguishable by the higher frequency, i.e., closer spacing of thepeaks, in the offset image marking process curve 610, shown as peaks tothe left of xerographic image marking process curve 620 in FIG. 8. Asecondary indicator identifying the xerographic image marking processcurve 620 is the high amplitude of the high frequency sidelobe at aperiodicity of 4-5 pixels.

FIG. 9 illustrates a functional block diagram of one exemplaryembodiment of the media/image marking process identification system 400according to this invention. The media/image marking processidentification system 400 may be a stand alone system or may beconnected to a network (not shown) via the link 414. The link 414 can beany known or later developed device or system for connecting themedia/image marking process identification system 400 to the network,including a connection over public switched telephone network, a directcable connection, a connection over a wide area network, a local areanetwork or a storage area network, a connection over an intranet or anextranet, a connection over the Internet, or a connection over any otherdistributed processing network or system. In general, the link 414 canbe any known or later-developed connection system or structure usable toconnect the media/image marking process identification system 400 to thenetwork.

As shown in FIG. 9, the media/image marking process identificationsystem 400 may include one or more display devices 470 usable to displayinformation to one or more users, and one or more user input devices 475usable to allow one or more users to input data into the media/imagemarking process identification system 400. The one or more displaydevices 470 and the one or more input devices 475 are connected to themedia/image marking process identification system 400 through aninput/output interface 410 via one or more communication links 471 and476, respectively, which are similar to the communication link 414above.

In various exemplary embodiments, the media/image marking processidentification system 400 includes one or more of a controller 420, amemory 430, an image data local variation differentiation circuit,routine or application 440, an image data spatial characteristicsdifferentiation circuit, routine or application 450, an image datafrequency distribution circuit, routine or application 460, an imagedata statistics generation circuit, routine or application 470, and amedia/image marking process determination circuit, routine orapplication 480, which are interconnected over one or more data and/orcontrol buses and/or application programming interfaces 492. The memory430 includes one or more of a media/image marking process identificationmodel 432.

The controller 420 controls the operation of the other components of themedia/image marking process identification system 400. The controller420 also controls the flow of data between components of the media/imagemarking process identification system 400 as needed. The memory 430 canstore information coming into or going out of the media/image markingprocess identification system 400, may store any necessary programsand/or data implementing the functions of the media/image markingprocess identification system 400, and/or may store data and/oruser-specific information at various stages of processing.

The memory 430 includes any machine-readable medium and can beimplemented using appropriate combination of alterable, volatile ornon-volatile memory or non-alterable, or fixed, memory. The alterablememory, whether volatile or non-volatile, can be implemented using anyone or more of static or dynamic RAM, a floppy disk and disk drive, awritable or re-rewriteable optical disk and disk drive, a hard drive,flash memory or the like. Similarly, the non-alterable or fixed memorycan be implemented using any one or more of ROM, PROM, EPROM, EEPROM, anoptical ROM disk, such as a CD-ROM or DVD-ROM disk, and disk drive orthe like.

In various exemplary embodiments, the media/image marking processidentification model 432 which the media/image marking processidentification system 400 employs to identify the media and/or imagemarking process used to process a particular medium is based on theimage data analysis techniques discussed above to determine localvariations of the input data, identify image data spatialcharacteristics, determine image data frequency distributions, and thelike.

With reference to FIGS. 1 and 9, the image data local variationdifferentiation circuit, routine or application 440 is activated by thecontroller 420 to differentiate between a continuous tone image markingproces's 120 and a halftone image marking process 125 in a scanned imageby examining local variations of the scanned image input data todetermine whether there is low local/spatial variation 115 in thescanned image data or high local/spatial variation 116 in the scannedimage data.

This distinction coincides with the distinction between a photograph orother analog image marking process and a binary image marking process.That is, determining continuous tone image data would imply that theimage marking process for the scanned image data is a photo process,i.e., that the image is a photo 121.

As discussed above, detecting a halftone marking process 125 would implythat the image marking process for the scanned image data is an ink-jetmarking process 140, a xerographic marking process 145, an offsetmarking process 146, or the like.

The image data spatial characteristics differentiation circuit, routineor application 450 is activated by the controller 420 to differentiatebetween the various halftone image marking processes 140, 145 and 146 byexamining the spatial characteristics of the scanned image data todetermine whether the data has a dispersed/aperiodic character 135 or aclustered/periodic character 136.

Detecting data having a dispersed/aperiodic character would imply thatthe image marking process for the scanned image data is an ink-jetmarking process 140, i.e., that the image is an ink-jet image 141. Onthe other hand, detecting data having a clustered/periodic characterwould imply that the image marking process for the scanned image data isa xerographic marking process 145, an offset marking process 146, or thelike.

The image data frequency distribution circuit, routine or application460 is activated by the controller 420 to differentiates between axerographic marking process 160 and an offset marking process 165 byexamining the data frequency distribution or internal structure of thescanned image data. Image data internal structure examples that may beconsidered include determining whether the image data has a linestructure as contrasted with a rotated structure, whether the halftonedots have a high frequency structure versus a low frequency structure,and whether the halftone screen noise is high or low.

Detecting image data having a low frequency/high noise character 155would imply that the image marking process for the scanned image data isa xerographic marking process 160 that was used to create a xerographicimage 161. On the other hand, detecting image data having a highfrequency/low noise character 156 would imply that the image markingprocess for the scanned image data is an offset, or lithographic,marking process 165 that was used to generate an offsetprinted/lithographic image 166.

The image data statistics generation circuit, routine or application 470is activated by the controller 420 to generate one or more datastatistics of the image data, as discussed above, which are then areanalyzed by one or more of the circuits, routines or applications 420,430, 440.

The media/image marking process determination circuit, routine orapplication 480 is activated by the controller 420 to determine the typeof image marking process used to process the image data evaluated oranalyzed.

A fully automated approach for detecting the input image marking processbased on the spatial statistics of the scanned image has been described.Because the spatial statistics of the scanned image are highlycorrelated with the underlying reproduction process, the methods andsystems according to various exemplary embodiments of the inventionallow for a reliable classification of the type of the image markingprocess. It is also well understood that any automated approach can beused in a semi-automatic fashion to aid a user, either by preferentiallyguiding user decisions, by setting system defaults, by alerting users todiscrepancies, or the like.

Although the above discussion first selects blocks of pixels to be usedfor image analysis, then creates statistical data indicative of amarking process, then creates a dispersion metric for the blocks, thencreates a periodicity metric, this order may be changed, especially ifthe input image marking processes have some sort of pre-classification.Moreover, because the metrics described above have been shown to besequentially derived, some classification decisions may be made earlierthan others. It should also be noted that a periodicity metric may alsobe considered to be a noise metric because a periodicity metric comparesamplitudes and harmonics.

While this invention has been described with reference to a colorscanner, the invention is not limited to such an embodiment. Theinvention may be applied to scanned image data captured at a remotelocation or to image data captured from a hard copy reproduction by adevice other than a scanner, for example a digital camera. The inventionmay be practiced on any color reproduction device, such as, for examplea color photocopier, and is also not intended to be limited to theparticular colors described above.

While this invention has been described in conjunction with specificembodiments outlined above, it is evident that many alternatives,modifications and variations will be apparent to those skilled in theart. Accordingly the preferred embodiments of the invention as set forthabove are intended to be illustrative and not limiting. Various changesmay be made without departing from the spirit and scope of the inventionas defined in the following claims.

1. A method of analyzing a printed image, comprising: scanning theprinted image; generating an event map for one or more input data blocksin the scanned printed image; determining data statistics of the printedimage from at least the event map generated; determining spatialvariations in the printed image based on the data statistics;determining a histogram and a frequency distribution based on the datastatistics; and determining an image marking process used to create theprinted image based on the determined spatial variations, the histogramand the frequency distribution in the printed image, wherein the eventmap is generated by determining frequency distribution of colors,generating a first color level set, and generating a first binary imageby setting dots pixels having the first color level set to 1 otherwiseto 0, or by generating a gray value for an image channel, calculating athreshold value for the image channel, generating a second color levelset, and generating a second binary image channel by setting pointpixels having the second color level set to 1 otherwise
 0. 2. The methodof claim 1, wherein spatial variations include local spatial variationsof the scanned image data.
 3. The method of claim 2, wherein a low valueof the local spatial variation of the scanned image data is indicativeof a photographic image marking process or background noise.
 4. Themethod of claim 2, wherein a high value of the local spatial variationof the scanned image data is indicative of a halftone image markingprocess.
 5. The method of claim 1, wherein the histogram includes aperiodicity/variance distribution.
 6. The method of claim 5, wherein adispered/aperiodic distribution of the scanned image data is indicativeof an inkjet image marking process.
 7. The method of claim 5, wherein aclustered/periodic distribution of the scanned image data is indicativeof a xerographic image marking process or an offset image markingprocess or a lithographic image marking process.
 8. The method of claim1, wherein spatial variations include at least one of an area varianceand an area variance to mean ratio.
 9. The method of claim 8, wherein axerographic image marking process has low screen frequency and highscreen noise characteristics.
 10. The method of claim 8, wherein anoffset or a Iithographic image marking process has high screen frequencyand low screen noise characteristics.
 11. The method of claim 1, whereinscanning the printed image comprises dividing scanned printed image intoimage data blocks.
 12. The method of claim 11, wherein scanning theprinted image further comprises selecting one or more image data blocks.13. The method of claim 1, wherein determining the image marking processbased on the determined spatial variations comprises determining atleast one set of data statistics for the scanned printed image.
 14. Themethod of claim 13, wherein determining at least one set of datastatistics comprises determining one or more of an area average or meanof pixels in an image data block of the scanned printed image, an areavariance of the pixels for the image data block, extreme minima value,min_(a), of the pixels for the image data block, extreme maxima value,max_(a), of the pixels for the image data block.
 15. The method of claim14, further comprising performing data evaluations using the determinedone or more data statistics.
 16. The method of claim 15, whereinperforming data evaluations comprises one or more of determining a ratioof the area variance to mean determined for a given block, calculating adistribution of the mean values for large pixel areas, comparing thecalculated mean value to the determined min_(a) and/or max_(a) values,and determining a distance between maxima/minima.
 17. The method ofclaim 16, wherein determining an image marking process comprisesdetermining histogram based on inter maxima/minima distances.
 18. Amethod of determining an image marking process used to create a printedimage, comprising: scanning the printed image; generating an event mapfor one or more input data blocks in the scanned printed image;determining data statistics of the printed image from at least thegenerated event map; determining local spatial variations in the printedimage based on the data statistics; determining a histogram and afrequency distribution based on the data statistics; and determining theimage marking process used to create the printed image based on thedetermined local spatial variations, the histogram and the frequencydistribution in the printed image, wherein an event map is generated bydetermining frequency distribution of colors, generating a first colorlevel set, and generating a first binary image by setting dots pixelshaving the first color level set to 1 otherwise to 0, or by generating agray value for an image channel, calculating a threshold value for theimage channel, generating a second color level set, and generating asecond binary image channel by setting point pixels having the secondcolor level set to 1 otherwise
 0. 19. The method of claim 18, whereinthe histogram includes at least a periodicity/variance distribution. 20.The method of claim 18, wherein spatial variations include at least oneof an area variance and an area variance to mean ratio.
 21. The methodof claim 18, wherein determining the image marking process based on thedetermined local spatial variations comprises determining one or moredata statistics for the scanned printed image.
 22. The method of claim21, wherein determining one or more data statistics comprisesdetermining one or more of an area average or mean of pixels in an imagedata block of the scanned printed image, an area variance of the pixelsfor the image data block, extreme minima value, min_(a), of the pixelsfor the image data block, extreme maxima value, max_(a), of the pixelsfor the image data block.
 23. The method of claim 22 further comprisingperforming data evaluations using the determined one or more datastatistics.
 24. The method of claim 23, wherein performing dataevaluations comprises one or more of: determining a ratio of the areavariance to mean determined for a given block, calculating adistribution of the mean values for large pixel areas, comparing thecalculated mean value to the determined min_(a) and/or max_(a) values,and determining a distance between maxima/minima, and determining thehistogram based on inter maxima/minima distances.
 25. Acomputer-readable medium that is encoded with computer-readableinstructions and provides instructions for determining an image markingprocess used to create a printed image, instructions, which whenexecuted by a processor, cause the processor to perform operationscomprising: scanning the printed image; generating an event map for oneor more input data blocks in the scanned printed image; determining datastatistics of the printed image from at least the event map generated;determining local spatial variations in the printed image based on thedata statistics; determining a histogram and a frequency distributionbased on the data statistics; and determining the image marking processused to create the printed image based on the determined local spatialvariations, the histogram and the frequency distribution in the printedimage. wherein an event map is generated by determining frequencydistribution of colors, generating a first color level set, andgenerating a first binary image by setting dots pixels having the firstcolor level set to 1 otherwise to
 0. or by generating a gray value foran image channel, calculating a threshold value for the image channel,generating a second color level set, and generating a second binaryimage channel by setting point pixels having the second color level setto 1 otherwise
 0. 26. The computer-readable medium according to claim25, wherein the histogram includes at least a periodicity/variancedistribution.
 27. The computer-readable medium according to claim 25,wherein spatial variations include at least one of an area variance andan area variance to mean ratio.
 28. The computer-readable mediumaccording to claim 25, wherein determining the image marking processbased on the determined local spatial variations comprises determiningone or more data statistics for the scanned printed image.
 29. Thecomputer-readable medium according to claim 28, wherein determining oneor more data statistics comprises determining one or more of an areaaverage or mean of pixels in an image data block of the scanned printedimage, an area variance of the pixels for the image data block, extrememinima value, min_(a), of the pixels for the image data block, extrememaxima value, max_(a), of the pixels for the image data block.
 30. Thecomputer-readable medium according to claim 29 further comprisingperforming data evaluations using the determined one or more datastatistics.
 31. The computer-readable medium according to claim 30,wherein performing data evaluations comprises one or more of:determining a ratio of the area variance to mean determined for a givenblock, calculating a distribution of the mean values for large pixelareas, comparing the calculated mean value to the determined min_(a)and/or max_(a) values, and determining a distance between maxima/minimaand determining the histogram based on inter maxima/minima distances.32. A media/image marking process identification system for a printedpage, comprising: a memory; and a media/image marking processidentification determination circuit, routine or application thatidentifies at least one of a media type for the printed page or an imagemarking process used to process the printed page, by processing theprinted page to generate an event map for one or more input data blocksin the printed image, determine data statistics of the printed imagefrom at least the event map generated; statistically analyzing the datastatistics of the printed image; determining local spatial variations inthe printed image based on the data statistics; determining a histogramand a frequency distribution based on the data statistics; anddetermining the media/image marking process used to create the printedimage based on the determined spatial variations, the histogram and thefrequency distribution in the printed image, wherein the event map isgenerated by determining frequency distribution of colors, generating afirst color level set, and generating a first binary image by settingdots pixels having the first color level set to 1 otherwise to 0, or bygenerating a gray value for an image channel, calculating a thresholdvalue for the image channel, generating a second color level set, andgenerating a second binary image channel by setting point pixels havingthe second color level set to 1 otherwise
 0. 33. The media/image markingprocess identification system according to claim 32, wherein localspatial variations include at least one of a variance and a variance toa mean ratio.
 34. The media/image marking process identification systemaccording to claim 32, wherein the histogram includes aperiodicity/variance distribution.
 35. The media/image marking processidentification system according to claim 32, wherein determining themedia/image marking process based on the determined local spatialvariations comprises determining one or more data statistics for thescanned printed image.
 36. The media/image marking processidentification system according to claim 35, wherein determining one ormore data statistics comprises determining one or more of an areaaverage or mean of pixels in an image data block of the scanned printedimage, an area variance of the pixels for the image data block, extrememinima value, min_(a), of the pixels for the image data block, extrememaxima value, max_(a), of the pixels for the image data block.
 37. Themedia/image marking process identification system according to claim 36further comprising performing data evaluations using the determined oneor more data statistics.
 38. The media/image marking processidentification system according to claim 37, wherein performing dataevaluations comprises one or more of: determining a ratio of the areavariance to mean determined for a given block, calculating adistribution of the mean values for large pixel areas, comparing thecalculated mean value to the determined min_(a) and/or max_(a) values,and determining a distance between maxima/minima, and determining thehistogram based on inter maxima/minima distances.